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Explainable and Transparent AI and Multi-Agent Systems

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Cover of 'Explainable and Transparent AI and Multi-Agent Systems'

Table of Contents

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    Book Overview
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    Chapter 1 Evaluation of Importance Estimators in Deep Learning Classifiers for Computed Tomography
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    Chapter 2 Integration of Local and Global Features Explanation with Global Rules Extraction and Generation Tools
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    Chapter 3 ReCCoVER: Detecting Causal Confusion for Explainable Reinforcement Learning
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    Chapter 4 Smartphone Based Grape Leaf Disease Diagnosis and Remedial System Assisted with Explanations
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    Chapter 5 Recent Neural-Symbolic Approaches to ILP Based on Templates
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    Chapter 6 On the Design of PSyKI: A Platform for Symbolic Knowledge Injection into Sub-symbolic Predictors
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    Chapter 7 The Mirror Agent Model: A Bayesian Architecture for Interpretable Agent Behavior
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    Chapter 8 Semantic Web-Based Interoperability for Intelligent Agents with PSyKE
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    Chapter 9 Case-Based Reasoning via Comparing the Strength Order of Features
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    Chapter 10 Explainability Metrics and Properties for Counterfactual Explanation Methods
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    Chapter 11 The Use of Partial Order Relations and Measure Theory in Developing Objective Measures of Explainability
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    Chapter 12 An Evaluation of Methodologies for Legal Formalization
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    Chapter 13 Risk and Exposure of XAI in Persuasion and Argumentation: The case of Manipulation
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    Chapter 14 Requirements for Tax XAI Under Constitutional Principles and Human Rights
Attention for Chapter 14: Requirements for Tax XAI Under Constitutional Principles and Human Rights
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Citations

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Chapter title
Requirements for Tax XAI Under Constitutional Principles and Human Rights
Chapter number 14
Book title
Explainable and Transparent AI and Multi-Agent Systems
Published by
Springer, Cham, January 2022
DOI 10.1007/978-3-031-15565-9_14
Book ISBNs
978-3-03-115564-2, 978-3-03-115565-9
Authors

Kuzniacki, Blazej, Almada, Marco, Tyliński, Kamil, Górski, Łukasz

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 7 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 29%
Student > Ph. D. Student 1 14%
Student > Master 1 14%
Unknown 3 43%
Readers by discipline Count As %
Business, Management and Accounting 2 29%
Economics, Econometrics and Finance 1 14%
Engineering 1 14%
Unknown 3 43%